OpenRaiser/Intern-Atlas huggingface dataset:
HLinear: Exact Dense Linear Algebra in Haskell
Venue: arXiv cs.MS 2017
Year: 2017

We present an implementation in the functional programming language Haskell of the PLE decomposition of matrices over division rings. Our benchmarks indicate that it is competitive with the C-based implementation provided in Flint. Describing the guiding principles of our work, we introduce the reader to basic ideas from high-performance functional programming.
Characterization and Inference of Graph Diffusion Processes from Observations of Stationary Signals
Venue: arXiv cs.DS 2017
Year: 2017

Many tools from the field of graph signal processing exploit knowledge of the underlying graph's structure (e.g., as encoded in the Laplacian matrix) to process signals on the graph. Therefore, in the case when no graph is available, graph signal processing tools cannot be used anymore. Researchers have proposed approaches to infer a graph topology from observations of signals on its nodes. Since the problem is ill-posed, these approaches make assumptions, such as smoothness of the signals on the graph, or sparsity priors. In this paper, we propose a characterization of the space of valid graphs, in the sense that they can explain stationary signals. To simplify the exposition in this paper, we focus here on the case where signals were i.i.d. at some point back in time and were observed after diffusion on a graph. We show that the set of graphs verifying this assumption has a strong connection with the eigenvectors of the covariance matrix, and forms a convex set. Along with a theoretical study in which these eigenvectors are assumed to be known, we consider the practical case when the observations are noisy, and experimentally observe how fast the set of valid graphs converges to the set obtained when the exact eigenvectors are known, as the number of observations grows. To illustrate how this characterization can be used for graph recovery, we present two methods for selecting a particular point in this set under chosen criteria, namely graph simplicity and sparsity. Additionally, we introduce a measure to evaluate how much a graph is adapted to signals under a stationarity assumption. Finally, we evaluate how state-of-the-art methods relate to this framework through experiments on a dataset of temperatures.
Facial Expression Recognition from World Wild Web
Venue: arXiv cs.CV 2017
Year: 2017

Recognizing facial expression in a wild setting has remained a challenging task in computer vision. The World Wide Web is a good source of facial images which most of them are captured in uncontrolled conditions. In fact, the Internet is a Word Wild Web of facial images with expressions. This paper presents the results of a new study on collecting, annotating, and analyzing wild facial expressions from the web. Three search engines were queried using 1250 emotion related keywords in six different languages and the retrieved images were mapped by two annotators to six basic expressions and neutral. Deep neural networks and noise modeling were used in three different training scenarios to find how accurately facial expressions can be recognized when trained on noisy images collected from the web using query terms (e.g. happy face, laughing man, etc)? The results of our experiments show that deep neural networks can recognize wild facial expressions with an accuracy of 82.12%.
A construction of $q$-ary linear codes with two weights
Venue: arXiv cs.IT 2017
Year: 2017

Linear codes with a few weights are very important in coding theory and have attracted a lot of attention. In this paper, we present a construction of $q$-ary linear codes from trace and norm functions over finite fields. The weight distributions of the linear codes are determined in some cases based on Gauss sums. It is interesting that our construction can produce optimal or almost optimal codes. Furthermore, we show that our codes can be used to construct secret sharing schemes with interesting access structures and strongly regular graphs with new parameters.
Perception-aware Path Planning
Venue: arXiv cs.RO 2017
Year: 2017

In this paper, we give a double twist to the problem of planning under uncertainty. State-of-the-art planners seek to minimize the localization uncertainty by only considering the geometric structure of the scene. In this paper, we argue that motion planning for vision-controlled robots should be perception aware in that the robot should also favor texture-rich areas to minimize the localization uncertainty during a goal-reaching task. Thus, we describe how to optimally incorporate the photometric information (i.e., texture) of the scene, in addition to the the geometric one, to compute the uncertainty of vision-based localization during path planning. To avoid the caveats of feature-based localization systems (i.e., dependence on feature type and user-defined thresholds), we use dense, direct methods. This allows us to compute the localization uncertainty directly from the intensity values of every pixel in the image. We also describe how to compute trajectories online, considering also scenarios with no prior knowledge about the map. The proposed framework is general and can easily be adapted to different robotic platforms and scenarios. The effectiveness of our approach is demonstrated with extensive experiments in both simulated and real-world environments using a vision-controlled micro aerial vehicle.
Efficiently making (almost) any concurrency control mechanism serializable
Venue: arXiv cs.DB 2017
Year: 2017

Concurrency control (CC) algorithms must trade off strictness for performance. Serializable CC schemes generally pay higher cost to prevent anomalies, both in runtime overhead and in efforts wasted by aborting transactions. We propose the serial safety net (SSN), a serializability-enforcing certifier which can be applied with minimal overhead on top of various CC schemes that offer higher performance but admit anomalies, such as snapshot isolation and read committed. The underlying CC retains control of scheduling and transactional accesses, while SSN tracks the resulting dependencies. At commit time, SSN performs an efficient validation test by examining only direct dependencies of the committing transaction to determine whether it can commit safely or must abort to avoid a potential dependency cycle. SSN performs robustly for various workloads. It maintains the characteristics of the underlying CC without biasing toward certain types of transactions, though the underlying CC might. Besides traditional OLTP workloads, SSN also allows efficient handling of heterogeneous workloads with long, read-mostly transactions. SSN can avoid tracking the majority of reads (thus reducing the overhead of serializability certification) and still produce serializable executions with little overhead. The dependency tracking and validation tests can be done efficiently, fully parallel and latch-free, for multi-version systems on modern hardware with substantial core count and large main memory. We demonstrate the efficiency, accuracy and robustness of SSN using extensive simulations and an implementation that overlays snapshot isolation in ERMIA, a memory-optimized OLTP engine that is capable of running different CC schemes. Evaluation results confirm that SSN is a promising approach to serializability with robust performance and low overhead for various workloads.
The Stable Roommates problem with short lists
Venue: arXiv cs.DM 2017
Year: 2017

We consider two variants of the classical Stable Roommates problem with Incomplete (but strictly ordered) preference lists SRI that are degree constrained, i.e., preference lists are of bounded length. The first variant, EGAL d-SRI, involves finding an egalitarian stable matching in solvable instances of SRI with preference lists of length at most d. We show that this problem is NP-hard even if d=3. On the positive side we give a (2d+3)/7-approximation algorithm for d={3,4,5} which improves on the known bound of 2 for the unbounded preference list case. In the second variant of SRI, called d-SRTI, preference lists can include ties and are of length at most d. We show that the problem of deciding whether an instance of d-SRTI admits a stable matching is NP-complete even if d=3. We also consider the "most stable" version of this problem and prove a strong inapproximability bound for the d=3 case. However for d=2 we show that the latter problem can be solved in polynomial time.
On the Mobile-to-Mobile Linear Time-Variant Shallow-Water Acoustic Channel Response
Venue: arXiv cs.IT 2017
Year: 2017

We expose some concepts concerning the channel impulse response (CIR) of linear time-varying (LTV) channels to give a proper characterization of the mobile-to-mobile underwater channel. We find different connections between the linear time-invariant (LTI) CIR of the static channel and two definitions of LTV CIRs of the dynamic mobile-to-mobile channel. These connections are useful to design a dynamic channel simulator from the static channel models available in the literature. Such feature is particularly interesting for overspread channels, which are hard to characterize by a measuring campaign. Specifically, the shallow water acoustic (SWA) channel is potentially overspread due the signal low velocity of propagation which prompt long delay spread responses and great Doppler effect. Furthermore, from these connections between the LTI static CIRs and the LTV dynamic CIRS, we find that the SWA mobile-to-mobile CIR does not only depend on the relative velocity between transceivers, but also on the absolute velocity of each of them referred to the velocity of propagation. Nevertheless, publications about this topic do not consider it and formulate their equations in terms of the relative velocity between transceivers. We illustrate our find using two couples of examples where, even though the relative velocity between the mobiles is the same, their CIRs are not.
Space-Time Tradeoffs for Distributed Verification
Venue: arXiv cs.DC 2017
Year: 2017

Verifying that a network configuration satisfies a given boolean predicate is a fundamental problem in distributed computing. Many variations of this problem have been studied, for example, in the context of proof labeling schemes (PLS), locally checkable proofs (LCP), and non-deterministic local decision (NLD). In all of these contexts, verification time is assumed to be constant. Korman, Kutten and Masuzawa [PODC 2011] presented a proof-labeling scheme for MST, with poly-logarithmic verification time, and logarithmic memory at each vertex. In this paper we introduce the notion of a $t$-PLS, which allows the verification procedure to run for super-constant time. Our work analyzes the tradeoffs of $t$-PLS between time, label size, message length, and computation space. We construct a universal $t$-PLS and prove that it uses the same amount of total communication as a known one-round universal PLS, and $t$ factor smaller labels. In addition, we provide a general technique to prove lower bounds for space-time tradeoffs of $t$-PLS. We use this technique to show an optimal tradeoff for testing that a network is acyclic (cycle free). Our optimal $t$-PLS for acyclicity uses label size and computation space $O((\log n)/t)$. We further describe a recursive $O(\log^* n)$ space verifier for acyclicity which does not assume previous knowledge of the run-time $t$.
Nonnegative Matrix Factorization Requires Irrationality
Venue: arXiv cs.CC 2017
Year: 2017

Nonnegative matrix factorization (NMF) is the problem of decomposing a given nonnegative $n \times m$ matrix $M$ into a product of a nonnegative $n \times d$ matrix $W$ and a nonnegative $d \times m$ matrix $H$. A longstanding open question, posed by Cohen and Rothblum in 1993, is whether a rational matrix $M$ always has an NMF of minimal inner dimension $d$ whose factors $W$ and $H$ are also rational. We answer this question negatively, by exhibiting a matrix for which $W$ and $H$ require irrational entries.
Embedding based on function approximation for large scale image search
Venue: arXiv cs.CV 2017
Year: 2017

The objective of this paper is to design an embedding method that maps local features describing an image (e.g. SIFT) to a higher dimensional representation useful for the image retrieval problem. First, motivated by the relationship between the linear approximation of a nonlinear function in high dimensional space and the stateof-the-art feature representation used in image retrieval, i.e., VLAD, we propose a new approach for the approximation. The embedded vectors resulted by the function approximation process are then aggregated to form a single representation for image retrieval. Second, in order to make the proposed embedding method applicable to large scale problem, we further derive its fast version in which the embedded vectors can be efficiently computed, i.e., in the closed-form. We compare the proposed embedding methods with the state of the art in the context of image search under various settings: when the images are represented by medium length vectors, short vectors, or binary vectors. The experimental results show that the proposed embedding methods outperform existing the state of the art on the standard public image retrieval benchmarks.
Backprop KF: Learning Discriminative Deterministic State Estimators
Venue: arXiv cs.LG 2017
Year: 2017

Generative state estimators based on probabilistic filters and smoothers are one of the most popular classes of state estimators for robots and autonomous vehicles. However, generative models have limited capacity to handle rich sensory observations, such as camera images, since they must model the entire distribution over sensor readings. Discriminative models do not suffer from this limitation, but are typically more complex to train as latent variable models for state estimation. We present an alternative approach where the parameters of the latent state distribution are directly optimized as a deterministic computation graph, resulting in a simple and effective gradient descent algorithm for training discriminative state estimators. We show that this procedure can be used to train state estimators that use complex input, such as raw camera images, which must be processed using expressive nonlinear function approximators such as convolutional neural networks. Our model can be viewed as a type of recurrent neural network, and the connection to probabilistic filtering allows us to design a network architecture that is particularly well suited for state estimation. We evaluate our approach on synthetic tracking task with raw image inputs and on the visual odometry task in the KITTI dataset. The results show significant improvement over both standard generative approaches and regular recurrent neural networks.
Recognizing Simple-Triangle Graphs by Restricted 2-Chain Subgraph Cover
Venue: arXiv cs.DM 2017
Year: 2017

A simple-triangle graph (also known as a PI graph) is the intersection graph of a family of triangles defined by a point on a horizontal line and an interval on another horizontal line. The recognition problem for simple-triangle graphs was a longstanding open problem, and recently a polynomial-time algorithm has been given [G. B. Mertzios, The Recognition of Simple-Triangle Graphs and of Linear-Interval Orders is Polynomial, SIAM J. Discrete Math., 29(3):1150--1185, 2015]. Along with the approach of this paper, we show a simpler recognition algorithm for simple-triangle graphs. To do this, we provide a polynomial-time algorithm to solve the following problem: Given a bipartite graph $G$ and a set $F$ of edges of $G$, find a 2-chain subgraph cover of $G$ such that one of two chain subgraphs has no edges in $F$.
Riemannian stochastic variance reduced gradient on Grassmann manifold
Venue: arXiv cs.LG 2017
Year: 2017

Stochastic variance reduction algorithms have recently become popular for minimizing the average of a large, but finite, number of loss functions. In this paper, we propose a novel Riemannian extension of the Euclidean stochastic variance reduced gradient algorithm (R-SVRG) to a compact manifold search space. To this end, we show the developments on the Grassmann manifold. The key challenges of averaging, addition, and subtraction of multiple gradients are addressed with notions like logarithm mapping and parallel translation of vectors on the Grassmann manifold. We present a global convergence analysis of the proposed algorithm with decay step-sizes and a local convergence rate analysis under fixed step-size with some natural assumptions. The proposed algorithm is applied on a number of problems on the Grassmann manifold like principal components analysis, low-rank matrix completion, and the Karcher mean computation. In all these cases, the proposed algorithm outperforms the standard Riemannian stochastic gradient descent algorithm.
Hijacking Bitcoin: Routing Attacks on Cryptocurrencies
Venue: arXiv cs.NI 2017
Year: 2017

As the most successful cryptocurrency to date, Bitcoin constitutes a target of choice for attackers. While many attack vectors have already been uncovered, one important vector has been left out though: attacking the currency via the Internet routing infrastructure itself. Indeed, by manipulating routing advertisements (BGP hijacks) or by naturally intercepting traffic, Autonomous Systems (ASes) can intercept and manipulate a large fraction of Bitcoin traffic. This paper presents the first taxonomy of routing attacks and their impact on Bitcoin, considering both small-scale attacks, targeting individual nodes, and large-scale attacks, targeting the network as a whole. While challenging, we show that two key properties make routing attacks practical: (i) the efficiency of routing manipulation; and (ii) the significant centralization of Bitcoin in terms of mining and routing. Specifically, we find that any network attacker can hijack few (<100) BGP prefixes to isolate ~50% of the mining power---even when considering that mining pools are heavily multi-homed. We also show that on-path network attackers can considerably slow down block propagation by interfering with few key Bitcoin messages. We demonstrate the feasibility of each attack against the deployed Bitcoin software. We also quantify their effectiveness on the current Bitcoin topology using data collected from a Bitcoin supernode combined with BGP routing data. The potential damage to Bitcoin is worrying. By isolating parts of the network or delaying block propagation, attackers can cause a significant amount of mining power to be wasted, leading to revenue losses and enabling a wide range of exploits such as double spending. To prevent such effects in practice, we provide both short and long-term countermeasures, some of which can be deployed immediately.
Online Placement of Multi-Component Applications in Edge Computing Environments
Venue: arXiv cs.DC 2017
Year: 2017

Mobile edge computing is a new cloud computing paradigm which makes use of small-sized edge-clouds to provide real-time services to users. These mobile edge-clouds (MECs) are located in close proximity to users, thus enabling users to seamlessly access applications running on MECs. Due to the co-existence of the core (centralized) cloud, users, and one or multiple layers of MECs, an important problem is to decide where (on which computational entity) to place different components of an application. This problem, known as the application or workload placement problem, is notoriously hard, and therefore, heuristic algorithms without performance guarantees are generally employed in common practice, which may unknowingly suffer from poor performance as compared to the optimal solution. In this paper, we address the application placement problem and focus on developing algorithms with provable performance bounds. We model the user application as an application graph and the physical computing system as a physical graph, with resource demands/availabilities annotated on these graphs. We first consider the placement of a linear application graph and propose an algorithm for finding its optimal solution. Using this result, we then generalize the formulation and obtain online approximation algorithms with polynomial-logarithmic (poly-log) competitive ratio for tree application graph placement. We jointly consider node and link assignment, and incorporate multiple types of computational resources at nodes.
Explicit constructions of optimal-access MDS codes with nearly optimal sub-packetization
Venue: arXiv cs.IT 2017
Year: 2017

An $(n,k,l)$ MDS array code of length $n,$ dimension $k=n-r$ and sub-packetization $l$ is formed of $l\times n$ matrices over a finite field $F,$ with every column of the matrix stored on a separate node in a distributed storage system and viewed as a coordinate of the codeword. Repair of a failed node can be performed by accessing a set of $d\le n-1$ helper nodes. The code is said to have the optimal access property if the amount of data accessed at each of the helper nodes meets a lower bound on this quantity. For optimal-access MDS codes with $d=n-1,$ the sub-packetization $l$ satisfies the bound $l\ge r^{(k-1)/r}.$ In our previous work, for any $n$ and $r,$ we presented an explicit construction of optimal-access MDS codes with sub-packetization $l=r^{n-1}.$ In this paper we take up the question of reducing the sub-packetization value $l$ to make it approach the lower bound. We construct an explicit family of optimal-access codes with $l=r^{\lceil n/r\rceil},$ which differs from the optimal value by at most a factor of $r^2.$ These codes can be constructed over any finite field $F$ as long as $|F|\ge r\lceil n/r\rceil,$ and afford low-complexity encoding and decoding procedures. We also define a version of the repair problem that bridges the context of regenerating codes and codes with locality constraints (LRC codes), calling it group repair with optimal access. In this variation, we assume that the set of $n=sm$ nodes is partitioned into $m$ repair groups of size $s,$ and require that the amount of accessed data for repair is the smallest possible whenever the $d$ helper nodes include all the other $s-1$ nodes from the same group as the failed node. For this problem, we construct a family of codes with the group optimal access property. These codes can be constructed over any field $F$ of size $|F|\ge n,$ and also afford low-complexity encoding and decoding procedures.
Information Measure as Time Complexity
Venue: arXiv cs.IT 2017
Year: 2017

The concept of Shannon entropy of random variables was generalized to measurable functions in general, and to simple functions with finite values in particular. It is shown that the information measure of a function is related to the time complexity of search problems concerning the functions in question. Formally, given a Turing reduction from a search problem f(x)=y to another function g(x), the amount of information about f(x)=y provided by querying g(x) is exactly equal to the average mutual information I(f;g). As a result, the average number of queries is I(f=y)/I(f;g), where I(f) is amount of self-information about the event {f=y}. In the idea case, if I(f=y)/I(f;g) is polynomial in the size of input and the function g(x) can be computed in polynomial time, then the problem f(x)=y also has polynomial-time algorithm. As it turns out, our information-based complexity estimation is a natural setting in which to study the power of randomized or probabilistic algorithms. Applying to decision problems, our result provides a strong evidence that P=RP=BPP. Using brute-force search as a benchmark for efficiency, our results also support that P!=PP. Further, applying an argument similar to Carnot's work on measuring the efficiency of an ideal heat engine in thermodynamics, it is shown that I(f=y)/H(f) is a lower bound on query complexity of solving f(x)=y. According to Markov's inequality, if I(f=y)/H(f) is exponential in the size of input, then the probability of f(x)=y having polynomial-time algorithm is a negative exponential in the size of input. This result enables us to reduce the problem of proving FP!=FNP to the computation of the entropy of certain simple functions, such as the subset sum function. As a result, P!=NP is provable, answering a long-standing open problem.